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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Robonaut 2 and You: Specifying and Executing Complex Operations

Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.

Baker, William↗
NASA NTRS2018

Evaluation of Crew-Centric Onboard Mission Operations Planning and Execution Tool: Year 2

Currently, mission planning for the International Space Station (ISS) is largely affected by ground operators in mission control. The task of creating a week-long mission plan for ISS crew takes dozens of people multiple days to complete, and is often created far in advance of its execution. As such, re-planning or adapting to changing real-time constraints or emergent issues is similarly taxing. As we design for future mission operations concepts to other planets or areas with limited connectivity to Earth, more of these ground-based tasks will need to be handled autonomously by the crew onboard.There is a need for a highly usable (including low training time) tool that enables efficient self-scheduling and execution within a single package. The ISS Program has identified Playbook as a potential option. It already has high crew acceptance as a plan viewer from previous analogs and can now support a crew self-scheduling assessment on ISS or on another mission. The goals of this work, a collaboration between the Human Research Program and the ISS Program, are to inform the design of systems for more autonomous crew operations and provide a platform for research on crew autonomy for future deep space missions. Our second year of the research effort have included new insights on the crew self-scheduling sessions performed by the crew through use on the HERA (Human Exploration Research Analog) and NEEMO (NASA Extreme Environment Mission Operations) analogs. Use on the NEEMO analog involved two self-scheduling strategies where the crew planned and executed two days of EVAs (Extra-Vehicular Activities). On HERA year two represented the first HERA campaign where we were able to perform research tasks. This involved selected flexible activities that the crew could schedule, mock timelines where the crew completed more complex planning exercises, usability evaluation of the crew self-scheduling features, and more insights into the limit of plan complexity that the crew could effectively self-schedule. In parallel we have added in new features and functionality in the Playbook tool based off of our insights from crew self-scheduling in the NASA analogs. In particular this year we have added in the ability for the crew to add, edit, and remove their own activities in the Playbook tool, expanding the type of planning and re-planning possible in the tool and opening up the ability for more free form plan creation. The ability to group and manipulate groups of activities from the plan task list was also added, allowing crew members to add predefined sets of activities onto their mission timeline. In addition we also added a way for crew members to roll back changes in their plan, in order to allow an undo like capability. These features expand and complement the initial self-scheduling features added in year one with the goal of making crew autonomous planning more efficient. As part of this work we have also finished developing the first version of our Playbook Data Analysis Tool, a research tool built to interpret and analyze the unobtrusively collected data obtained during the NASA analog missions through Playbook. This data which includes user click interaction as well as plan change information, through the Playbook Data Analysis Tool, allows us to playback this information as if a video camera was mounted over the crewmember's tablet. While the primary purpose of this tool is to allow usability analysis of crew self-scheduling sessions used on the NASA analog, since the data collected is structured, the tool can automatically derive metrics that would be traditionally tedious to achieve without manual analysis of video playback. We will demonstrate and discuss the ability for future derived metrics to be added to the tool. In addition to the current data and results gathered in year two we will also discuss the preparation and goals of our International Space Station (ISS) onboard technology demonstration with Playbook. This technology demonstration will be preformed as part of the CAST payload starting in late 2016.

onboard planning↗
NASA NTRSDate not supplied

AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. This poster presents AssistTaxi, which is a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Additionally, the dataset serves as a valuable resource for validating and enhancing existing algorithms as well as facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.

Data Collection↗
DOE OSTI2023

Integrating Learning and Physics based Computation for Fast Online Transient Analysis

In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Accelerating Collective Communication in Data Parallel Training across Deep Learning Frameworks

This work develops new techniques within Horovod, a generic communication library supporting data parallel training across deep learning frameworks. In particular, we improve the Horovod control plane by implementing a new coordination scheme that takes advantage of the characteristics of the typical data parallel training paradigm, namely the repeated execution of collectives on the gradients of a fixed set of tensors. Using a caching strategy, we execute Horovod’s existing coordinator-worker logic only once during a typical training run, replacing it with a more efficient decentralized orchestration strategy using the cached data and a global intersection of a bitvector for the remaining training duration. Next, we introduce a feature for end users to explicitly group collective operations, enabling finer grained control over the communication buffer sizes. To evaluate our proposed strategies, we conduct experiments on a world-class supercomputer — Summit. We compare our proposals to Horovod’s original design and observe 2x performance improvement at a scale of 6000 GPUs; we also compare them against tf.distribute and torch.DDP and achieve 12% better and comparable performance, respectively, using up to 1536 GPUs; we compare our solution against BytePS in typical HPC settings and achieve about 20% better performance on a scale of 768 GPUs. Finally, we test our strategies on a scientific application (STEMDL) using up to 27,600 GPUs (the entire Summit) and show that we achieve a near-linear scaling of 0.93 with a sustained performance of 1.54 exaflops (with standard error +- 0.02) in FP16 precision.

Romero, Joshua↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
NASA NTRS2014

Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant ongoing/completed bed rest and space flight studies. These data will be combined with predictor metrics that will be collected prospectively - behavioral, brain imaging and genomic measures; from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions against decrements in post-mission adaptive capability. This ability will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗
DOE OSTI2025

A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials

Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, in this study, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.

Kim, Dongjin [University of California, Berkeley, ↗
DOE OSTI2024

Kansas City, Missouri, Streetlight Electric Vehicle Charging: Strategies and challenges for site selection of streetlight electric vehicle infrastructure in Kansas City, Missouri (Final Report)

Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗
DOE OSTI2025

Operation of helium sub-atmospheric multistage cryogenic centrifugal compressor trains: Part 1 – Steady state modeling and speed ratio selection

Helium cryogenic systems which can provide cooling below the normal boiling point of helium (approximately 4.2 K) are often required by superconducting radio-frequency niobium resonators utilized in modern high-energy particle accelerators. Achieving temperatures below 4.2 K generally involves operating a cryogenic vessel with liquid helium under sub-atmospheric conditions, thereby lowering the saturation pressure and corresponding saturation temperature. Over the last several decades, multi-stage cryogenic centrifugal compressor trains (CC’s) have been operated efficiently and reliably within large-scale cryogenic systems to continuously evacuate helium vapor generated by a device within the vessel, maintaining sub-atmospheric conditions in the vessel while pressurizing the return vapor to above atmospheric conditions. Traditionally, these CC systems have been operated using empirically derived control philosophies and insight gathered from previous operational experience. Recent efforts at the Facility for Rare Isotope Beams (FRIB) have been aimed at the development of a theoretical basis to characterize the operation of multi-stage cryogenic centrifugal compressor train and utilizing predictive model results to generate control parameters. The objective of this research was identifying operational points which adequately balance cryogenic system efficiency, stability, and overall ease of operation. Furthermore, this manuscript provides an overview of the predictive model development, characterization of the FRIB cryogenic centrifugal compressors and implementation of the predicted performance results during steady-state system operation.

Compressor train control↗
NASA NTRS2009

Crew Management Processes Revitalize Patient Care

In 2005, two physicians, former NASA astronauts, created LifeWings Partners LLC in Memphis, Tennessee and began using Crew Resource Management (CRM) techniques developed at Ames Research Center in the 1970s to help improve safety and efficiency at hospitals. According to the company, when hospitals follow LifeWings? training, they can see major improvements in a number of areas, including efficiency, employee satisfaction, operating room turnaround, patient advocacy, and overall patient outcomes. LifeWings has brought its CRM training to over 90 health care organizations and annual sales have remained close to $3 million since 2007.

Source record↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Industrial Assessment Center – integration of education and practice. Final progress report

The goal of the DOE’s Industrial Assessment Center (IAC) program is twofold: first, to help US manufacturing competitiveness by providing assessments and recommendations for small and medium-sized enterprises (SMEs) on energy efficiency, productivity, sustainability and competitiveness – including measuring the impacts of these recommendations on reducing greenhouse gas emissions; and second, to address a growing shortage of engineering professionals with applied energy and manufacturing-related skills by training a diverse cross-section of engineering students through hands-on involvement in these assessments. IUPUI has an IAC, which was established in 2011, in the Department of Mechanical and Energy Engineering, Purdue School of Engineering and Technology, IUPUI. The IAC has won the awards twice. We have built the center that has the expertise and infrastructure for quality energy assessments to manufactures and commercial building owners and aligned our current research and teaching activities to the DOE’ training mission with programs to train the next generation of industrial energy efficiency experts, with theory and hands-on experience in conducting energy assessment for small and median manufacturing enterprises. We have made recommendations that have the potential to save about $20M annually. We also initiated research projects to meet DOE priorities, specifically in the areas of smart manufacturing and cybersecurity. On the energy assessment side, the Center has provided energy assessments to 144 qualified companies, primarily located in Indiana. On the training side, the center has trained students in various programs, such as the department’s Bachelor of Science (BS) programs in Mechanical Engineering (ME) and BS in Energy Engineering (EEN), both are ABET accredited engineering programs, as well as a graduate level Energy Management and Assessment certificate program. At the present, the center has trained 95 students, 43 or them received the DOE issued certificates. Research projects were developed within the center to advance energy efficiency related technologies, which provided excellent opportunities for our trainees. The center has also been building a professional network with utilities, Manufacturing Extension Partnership (MEP), government agencies, and manufacturing companies, to increase our client base and promote collaborations. The center received the 2019 Center of Excellence of the Year Award and three students received the “Outstanding Achievement in Energy Engineering by an IAC Student” awards in the past five years.

42 ENGINEERING↗
DOE OSTI2025

Sequence length scaling in vision transformers for scientific images on frontier

Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text have inspired scaling sequence lengths in ViTs, adapting these for ViTs introduces unique challenges. We develop distributed sequence parallelism for ViTs, enabling them to handle up to 1M tokens. Our approach, leveraging DeepSpeed-Ulysses and Long-Sequence-Segmentation with model sharding, is the first to apply sequence parallelism in ViT training, achieving a 94% batch scaling efficiency on 2,048 AMD-MI250X GPUs. Evaluating sequence parallelism in ViTs, particularly in models up to 10B parameters, highlighted substantial bottlenecks. We countered these with hybrid sequence, pipeline, and flash attention strategies, to scale beyond single GPU memory limits. Our method significantly enhances climate modeling accuracy by 20% in temperature predictions, marking the first training of a vision transformer model to convergence with a sequence length of 188K tokens, using full self-attention.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
NASA NTRSDate not supplied

Artemis Crewed Surface Operations Training Dust Mitigation Test/Training Facility (DMTF)

Based on several Apollo mission(s) reports and lessons learned1 on lunar dust impacts, it is imperative for successful Artemis missions, to develop dust mitigation methods & train crew in dust cleaning techniques for efficient use of lunar surface time and for crew health. Longer durations and increased number of Extra Vehicular Activities (EVAs) for Artemis need better training. Apollo missions lacked training in a dust facility. Current facilities do not provide adequate fidelity to train crew in dust cleaning operations in suited, pressurized environments. Mimicking dust cleaning actions in existing facilities (eg- Neutral Buoyancy lab) does not provide in depth understanding of actions and time needed to clean suit or positions crew need to take to remove dust from suit. Available dust bins do not support suited/pressurized ops, personnel not trained in suited ops, more overhead/travel. A local facility at Johnson Space Center (JSC) w/ adequate fidelity supporting suited/pressurized ops, personnel in suited ops is a crucial need for rapid development/testing and training. This facility will provide NASA capabilities to: 1. Provide a dust containment bin to perform suited pressurized crewed training/testing activities for dust interaction/mitigation leveraging existing JSC facility infrastructure and 2. Perform end-to-end simulated lunar dust testing of fluid, mechanical, and electrical systems to provide “test like you fly” confidence for industry developed solutions.

EVA training↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗